
"""simple docstring"""



import os
import random
import sys

from . import cryptomath_module as cryptoMath  # noqa: N812
from . import rabin_miller as rabinMiller  # noqa: N812


def 			_UpperCamelCase						(    )     ->	None:





    """simple docstring"""

    print("""Making key files..."""   )
    make_key_files("""rsa"""       ,   1_0_2_4   )
    print("""Key files generation successful."""   )


def 			_UpperCamelCase						(    _A   )     ->	tuple[tuple[int, int], tuple[int, int]]:





    """simple docstring"""

    print("""Generating prime p..."""   )
    _UpperCAmelCase        = rabinMiller.generate_large_prime(_A   )
    print("""Generating prime q..."""   )
    _UpperCAmelCase        = rabinMiller.generate_large_prime(_A   )
    _UpperCAmelCase        = p * q

    print("""Generating e that is relatively prime to (p - 1) * (q - 1)..."""   )
    while True:
        _UpperCAmelCase        = random.randrange(2 ** (key_size - 1)       ,   2 ** (key_size)   )
        if cryptoMath.gcd(_A       ,   (p - 1) * (q - 1)   ) == 1:
            break

    print("""Calculating d that is mod inverse of e..."""   )
    _UpperCAmelCase        = cryptoMath.find_mod_inverse(_A       ,   (p - 1) * (q - 1)   )

    _UpperCAmelCase        = (n, e)
    _UpperCAmelCase        = (n, d)
    return (public_key, private_key)


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	None:





    """simple docstring"""

    if os.path.exists(F"""{name}_pubkey.txt"""   ) or os.path.exists(F"""{name}_privkey.txt"""   ):
        print("""\nWARNING:"""   )
        print(
            F"""\"{name}_pubkey.txt\" or \"{name}_privkey.txt\" already exists. \n"""
            """Use a different name or delete these files and re-run this program."""   )
        sys.exit()

    _UpperCAmelCase     ,_UpperCAmelCase        = generate_key(_A   )
    print(F"""\nWriting public key to file {name}_pubkey.txt..."""   )
    with open(F"""{name}_pubkey.txt"""       ,   """w"""   ) as out_file:
        out_file.write(F"""{key_size},{public_key[0]},{public_key[1]}"""   )

    print(F"""Writing private key to file {name}_privkey.txt..."""   )
    with open(F"""{name}_privkey.txt"""       ,   """w"""   ) as out_file:
        out_file.write(F"""{key_size},{private_key[0]},{private_key[1]}"""   )


if __name__ == "__main__":
 main()

"""simple docstring"""



import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
a       :						Any       		=		logging.get_logger(__name__)


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	int:





    """simple docstring"""

    _UpperCAmelCase        = RobertaPreLayerNormConfig.from_pretrained(
        _A       ,   architectures=["""RobertaPreLayerNormForMaskedLM"""]   )

    # convert state_dict
    _UpperCAmelCase        = torch.load(hf_hub_download(repo_id=_A       ,   filename="""pytorch_model.bin"""   )   )
    _UpperCAmelCase        = {}
    for tensor_key, tensor_value in original_state_dict.items():
        # The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
        if tensor_key.startswith("""roberta."""   ):
            _UpperCAmelCase        = """roberta_prelayernorm.""" + tensor_key[len("""roberta."""   ) :]

        # The original implementation contains weights which are not used, remove them from the state_dict
        if tensor_key.endswith(""".self.LayerNorm.weight"""   ) or tensor_key.endswith(""".self.LayerNorm.bias"""   ):
            continue

        _UpperCAmelCase        = tensor_value

    _UpperCAmelCase        = RobertaPreLayerNormForMaskedLM.from_pretrained(
        pretrained_model_name_or_path=_A       ,   config=_A       ,   state_dict=_A   )
    model.save_pretrained(_A   )

    # convert tokenizer
    _UpperCAmelCase        = AutoTokenizer.from_pretrained(_A   )
    tokenizer.save_pretrained(_A   )


if __name__ == "__main__":
 a       :						Tuple       		=		argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--checkpoint-repo''',
     default=None,
     type=str,
     required=True,
     help='''Path the official PyTorch dump, e.g. \'andreasmadsen/efficient_mlm_m0.40\'.''',
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 a       :						Union[str, Any]       		=		parser.parse_args()
 convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)